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Solve a task before seeing its solution. Checks use multiple datasets and accept equivalent formulas.
90 guided challenges · challenge checks are recorded separately from lesson practice
Each saved pass records whether help was opened in that challenge attempt. A pass without opened help is not proof of authorship or general mastery.
Find and flag duplicate values →
Mark every repeated registration, keep blank rows blank, and see which values need a closer look.
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Join text fields without extra spaces →
Build a readable name or label from three columns, even when the middle field is missing.
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Sum amounts that meet several conditions →
Add paid sales for one region and date window, while keeping refunds and boundary dates in view.
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Count records that meet several conditions →
Count open tickets for one team within an age limit, without including rows that pass only one test.
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Count distinct values and show the list →
See which customer names are distinct, exclude blank rows, and count the resulting list.
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Calculate percentage change from a positive base →
Compare old and new values, distinguish growth from decline, and handle zero or negative starting values explicitly.
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Look up a price by an exact ID →
Enter a product ID, return its price, and handle missing or repeated IDs without hiding the rule.
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Fill formulas across and down with fixed references →
Build a price scenario table while keeping the base-price column, rate row and fixed fee in the right places.
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Clean spaces and line breaks in imported text →
Turn messy imported labels into readable text while making the cleaning rules explicit.
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Convert imported text into numbers →
Convert amounts with an explicit decimal convention, keep missing values blank, and flag text that cannot be parsed.
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Average the records that qualify →
Choose the intended region and delivery state, keep real zeros, and distinguish missing measurements from observations.
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Weight a workload by its case counts →
Combine per-case estimates using the number of cases instead of giving every service the same influence.
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Classify deliveries against an SLA →
Separate unfinished deliveries, missing measurements and genuine zero-hour observations before deciding whether a completed delivery is late.
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Build an operational review with the right denominator →
Compare two legitimate averages for one selected population and base the review flag on the agreed case-weighted metric.
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Find a value at a row and column intersection →
Use two labels to retrieve net units from a small matrix, even after rows and month columns move.
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Choose a rate from quantity bands →
Find the rate attached to the largest threshold at or below each quantity, with explicit rules for missing bands and negative inputs.
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Schedule dates with a working calendar →
Move forward or backward by working days, with a configurable weekend and a separate office-closure list.
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Count eligible dates between two endpoints →
Count both endpoints when they are working dates, preserve the sign of reversed intervals, and avoid double-counting closures.
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Choose a working cutoff at month end →
Shift to a target month, locate its calendar end, then move backward to the final eligible date.
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Build a filtered priority queue with a stable tie rule →
Select one team at or above a points threshold, then sort the complete records by points descending and numeric ticket ascending.
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Calculate every running balance with SCAN →
Start from an explicit opening balance and return the state after each signed entry, in source order.
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Return one numeric review mean for every record →
Use BYROW to reduce each three-review row, then keep the record labels beside the results with HSTACK.
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Summarise a numeric sample without hiding missing data →
Keep the numeric count, median and sample standard deviation together; distinguish a missing sample from a measured zero.
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Compare a chosen percentile with an allowance →
Calculate an inclusive percentile, count observations strictly above it and apply an explicit allowance rule.
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Flag observations outside the IQR fences →
Calculate inclusive quartiles and two 1.5-IQR fences, then count measured values strictly outside them.
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Split a pasted log into rows and fields →
Turn a small literal pipe-delimited log into a rectangular report while keeping empty records, blank fields and leading zeroes.
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Extract both sides of the final separator →
Use the last literal separator to separate a record prefix from its final field without losing earlier separators or repeated records.
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Normalize and review structured reference codes →
Create a three-column audit that validates a declared ASCII code shape before extracting a four-character suffix.
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Calculate a periodic loan payment with consistent units →
Build a signed fixed-payment model, then reconcile total outlay with the amount borrowed.
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Split a selected loan payment into interest and principal →
Inspect one end-of-period payment and prove that its signed components add to the fixed payment.
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Reconcile remaining loan balance after scheduled payments →
Connect the remaining balance, principal repaid and interest paid under an unrounded end-payment schedule.
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Sum repeated payments for each invoice →
Combine split receipts and refunds before calculating each invoice balance; keep unmatched receipts visible in the source.
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List required IDs missing from another export →
Build an ordered set difference with exact matches, normalized letter case and one output per missing identifier.
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Reconcile grouped totals from two exports →
Keep the union of both ID lists, combine signed repeated rows on each side, then report both totals and their difference.
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Group posted net units by region →
Filter order status before summing repeated regions, including returns and numeric zeroes.
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Cross-tabulate posted order means →
Build a region-by-channel mean report whose row, column and grand means use the original records.
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Report each group share of net units →
Make the grand-total denominator explicit, retaining signed returns, missing intersections and a zero-net state.
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Convert fixed-width local date and time text →
Build a real numeric timestamp from explicit components, then reject values that normalize to a different date or clock time.
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Calculate hours for a shift across midnight →
Use one-day clock wrap, whole-minute arithmetic and a declared break rule to calculate numeric paid hours.
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Count dates shared by two inclusive intervals →
Clip each original interval to a reporting window, count both endpoints and preserve a distinct review result for reversed records.
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Combine exports with different column orders →
Align ID, Team and Units before stacking two exports, preserving duplicate records and signed quantities.
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Flatten numeric readings in a declared scan order →
Turn a 4 by 3 readings grid into one column while keeping numeric zeroes and excluding text notes and booleans.
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Return the last N active records →
Skip empty-ID slots, reorder the fields and take the final requested records without sorting or reversing them.
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Calculate rounded charges for paired records →
Map quantity, rate and adjustment together, retaining one rounded result for every record.
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Accumulate posted charges after rounding each line →
Use a declared zero initial state, select posted records and add individually rounded signed charges.
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Build a carrier by parcel shipping quote matrix →
Generate every selected carrier/weight combination using included weight and half-kilogram billing increments.
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Classify real errors without misreading text →
Return a category and classic error code for each imported value while preserving source order.
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Replace only missing-value errors →
Use one literal fallback for real #N/A while keeping other errors visible and preserving ordinary value types.
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Summarize measurements that contain errors →
Report a signed total, numeric count, numeric mean and genuine error count without converting other value types to zero.
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Summarize records selected by a criteria table →
Read AND/OR criteria from cells and keep the numeric population visible.
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Retrieve exactly one database record →
Separate zero, one and multiple matching records while preserving the selected field type.
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Describe the numeric records selected by criteria →
Report mean and extremes for the actual selected numeric observations.
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Evaluate a periodic project without discounting time zero →
Separate an initial cost, unequal end-period receipts and the yield that makes the complete schedule balance.
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Evaluate dated cash flows using actual day intervals →
Keep date-value pairs aligned and use 365-day discounting for an irregular cash-flow schedule.
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Explain a modified return with finance and reinvestment rates →
Separate the present cost of negative flows from the terminal value of positive flows, then reconcile MIRR.
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Compare annual charges and the remaining carrying value →
Use SLN and SYD on the same cost, salvage and useful life, then reconcile cumulative SYD allocation.
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Compare fixed and accelerated declining balances →
Keep DB rate rounding, first-year months, DDB factors and the final fiscal stub explicit.
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Allocate a partial-year window from one fixed origin →
Reconcile fractional-year VDB allocations and closing values with and without straight-line switching.
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Fit two predictors and inspect the residuals →
Estimate one response from two paired predictors, then reconcile the model’s coefficients, new estimate and fit diagnostics.
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Fit a per-unit multiplier and a new level →
Fit a positive response across actual period gaps, then separate the multiplier, fractional change and log-space residual diagnostics.
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Compare exact, cumulative and interval event counts →
Build a complete binomial report for a fixed number of independent trials with one constant event probability.
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Compare finite sampling with a replacement model →
Calculate a sample-count report without replacement, then compare it with independent draws at the same starting proportion.
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Scale an event-count model to its observation window →
Convert a rate per base interval to a window mean, then report exact, cumulative, interval and upper-tail event probabilities.
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Estimate a mean with a two-sided t interval →
Report the sample mean, sample variation, confidence margin and both endpoints instead of presenting the margin as the whole interval.
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Compare changes in the same matched records →
Preserve record alignment, summarize after-minus-before differences and reconcile a two-sided paired test with a change interval.
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Compare independent groups without pooling their variances →
Keep unequal group sizes and sample variances separate, retain fractional Welch degrees of freedom and report a two-sided comparison.
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Reconcile clean price, accrued interest and settlement cash →
Build G2:G9 from the actual terms in B2:B9.
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Infer yield from a coupon bond quote →
Build G2:G7 from B2:B9.
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Compare duration with exact bond repricing →
Build G2:G9 using the fixed 100 redemption and terms in B2:B8 plus decimal yield change B10.
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Look up a rate with multiple criteria →
Match three fields on the same row, return the first rate, and count complete matches before treating the key as unique.
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Cross a column with a horizontal array →
Use one formula to expand three source amounts across four multipliers while keeping row and column meaning.
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Audit row-wise Boolean masks and counts →
Convert real Booleans to 0/1, combine row conditions with AND and OR, and reconcile counts without double-counting overlaps.
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Return three fields for a batch of lookup queries →
Look up four requested keys in order, preserve repeated requests and return the first exact record with an explicit missing policy.
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Copy expanding ranges into complete running totals →
Keep the first source row fixed, move the endpoint with each entry and copy the same seed across two signed measures.
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Refactor a nested IF policy into a maintained rule table →
Compare a frozen multi-branch policy with a current editable lower-bound table, then identify every request whose selected flat rate changes.
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Compose a numeric quote and formatted business labels →
Keep a reusable numeric quote beside customer-facing currency, signed variance, discount, date and quantity labels that update with its assumptions.
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Rebuild dates and measure complete calendar intervals →
Extract and rebuild real date components, compare DATE overflow with month-end clamping, then report complete years/months and actual elapsed days.
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Advance clock components and retain whole-day carry →
Rebuild a whole-second clock, advance its hour/minute/second components and keep rollover separate from actual elapsed time.
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Generate a configurable month-end date series →
Spill consecutive, quarterly or annual month ends from a numeric date, with an explicit first offset, length and month step.
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Rank records without sorting the source →
Compare both rank directions and averaged ties beside the untouched source, then select configurable kth extrema.
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Look up fields across a horizontal source →
Match literal identifiers across a header row and return the requested measure, preserving first-match, zero and missing policies.
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Compare OFFSET and INDEX dynamic ranges →
Use one row-count input to resize two real reference methods, then compare their totals, means and selected populations.
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Choose wildcard searches or literal product codes →
Compare intentional pattern matching with literal identifiers, returning the first physical source record and preserving numeric zero.
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Compare MATCH modes and maintained groups →
Inspect exact, nearest-lower and nearest-upper positions from differently ordered sources, then read the selected category from its actual row.
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Count numbers, text, logical values and blank-like cells →
Reconcile four disjoint stored types and explain why COUNTA plus COUNTBLANK can exceed the number of cells.
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Compare mean populations without changing stored types →
Keep numeric zeroes, excluded text and explicit mean denominators visible before choosing an averaging rule.
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Build and inspect line-separated address labels →
Use actual LF characters, compare compact and fixed-slot joins, then inspect an ASCII marker without losing the original text.
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Compare upper, lower, proper and preserved text →
Compare three capitalization methods beside the untouched source, reveal case-sensitive changes and choose a policy explicitly.
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Resolve overlapping request rules with explicit priority →
Compare nested IF and first-match IFS on the same requests. Combine AND, OR and NOT, expose overlapping rules and change the priority in a real worksheet cell.
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Compare formula operators and their evaluation order →
Compare all four operator classes on real inputs: numeric order, Boolean comparisons, text joining and range/union/intersection references, plus an actual spill helper and row-dependent implicit intersection.
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